ELE Times

“eMemory’s enhanced OTP products complement Cadence’s Securyzr Root of Trust within the Cadence Chiplet Framework. As a leading provider of non-volatile memory technologies, the combination of eMemory technology and Cadence’s security subsystem results in a Physical AI Chiplet platform delivering secure storage and long-lifecycle key management, reinforcing the strong hardware foundation provided by Cadence for die-to-die security and safety in advanced chiplet designs.”

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• Secure firmware authentication: firmware code is digitally signed and authenticated by NVIDIA, helping to maintain platform integrity.
• Root of Trust for system boot: cryptographic verification of the firmware using Elliptic Curve Cryptography (ECC-P384) public key technology. This establishes the root of trust for the entire laptop, which is critical because the EC is the first device to power on and authorise secure system boot.
• Advanced power management: handles battery charging, alerts and system power state transitions to optimise energy efficiency.
• System control: oversees key scan and keypad operations for reliable user input.
• New host interface support: implements packet command format processing unique to the NVIDIA DGX interface, advancing beyond traditional byte-level data transfers.
• Value-added integration: incorporates Electromagnetic Interference (EMI) and Static Random-Access Memory (SRAM) interfaces to improve overall system performance.

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Infineon Technologies AG and HL Klemove aim to strengthen their strategic collaboration in automotive technologies. Their partnership aims to combine Infineon’s semiconductor expertise and system understanding with HL Klemove’s capabilities in advanced autonomous driving systems to accelerate innovation in vehicle electronic architecture for the Software-Defined Vehicle (SDV) era and advance autonomous driving technologies.

This collaboration reflects the shared commitment of both companies to delivering safe and efficient connected mobility solutions. By optimising resources and accelerating proof of concept development, the partners aim to bring innovative technologies to market faster. Together, they plan to build the foundation for future key projects with high-performance, highly reliable autonomous driving solutions that combine Infineon’s semiconductor expertise and HL Klemove’s system integration capabilities.

Under the MoU, the two companies will cooperate in key areas, including:

• Next-generation Zonal Control Units: The companies will jointly develop zone controller applications using Infineon’s microcontrollers and power semiconductors. HL Klemove will lead application development, while Infineon provides semiconductor technology support. Through prototype development, the collaboration aims to strengthen competitiveness in SDV electronic architecture.
• Next-generation Radar Technologies: HL Klemove will leverage Infineon’s radar semiconductor solutions to develop high-resolution and short-range satellite radar, preparing for commercialisation through proof of concept. Additionally, the companies will work on high-resolution imaging radar to achieve next-generation radar technologies capable of precise object recognition.
• Vehicle Ethernet-based ADAS and Camera Solutions: The partners will cooperate on developing front camera modules and an ADAS parking control unit using Infineon’s Ethernet technology. HL Klemove will handle system and product development, while Infineon provides Ethernet semiconductor and networking technology to enable high-speed, highly reliable in-vehicle network solutions.

“Based on our holistic product portfolio, deep system understanding and application know-how, Infineon aims to empower the automotive industry to accelerate time-to-market of software-defined vehicles,” said Peter Schaefer, Executive Vice President and CSO Automotive of Infineon. “Our collaboration with HL Klemove combines Infineon’s technology leadership with HL Klemove’s system expertise to deliver safer and smarter mobility solutions.”

Yoon-Haeng Lee, CEO of HL Klemove, said, “This collaboration marks an important milestone in realising the next-generation electronic architecture required for the software-defined vehicle era. By combining HL Klemove’s system architecture and integration capabilities with Infineon’s semiconductor technology, we will accelerate innovation in key areas such as next-generation zonal controllers, vehicle Ethernet-based ADAS systems, and high-resolution radar.”

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Rohde & Schwarz, a world leader in AI-based millimetre wave screening technology, announced today it has won a multi-million dollar award from TSA to supply its QPS201 AIT security scanners to passenger security screening checkpoints at selected Soccer World Cup 2026 host city airports.

“We are thrilled to receive this award to deliver QPS201’s high-volume and passenger-friendly on-person security screening technology to modernize checkpoints at the airports of cities hosting the matches,” said Frank Dunn, CEO of Rohde & Schwarz USA, Inc. “TSA’s continued investment in the QPS will also further expand Rohde & Schwarz’s economic impact as we grow and create jobs at our facilities in Maryland and Texas.”

“We are proud that TSA is investing in modernising security checkpoints at the Soccer World Cup 2026 host city airports with our high-performance QPS201 technology platform,” said Andreas Haegele, Vice President of Microwave Imaging. “Rohde & Schwarz is deeply committed to our partnership with TSA. We will continue to develop and deliver innovative and effective on-person screening solutions to make airport security more efficient and convenient in the upcoming mega decade of travel, including the Soccer World Cup, America’s 250th Anniversary and the Olympic Games.”

The QPS201 achieved TSA qualification in 2022, approving it for use in US passenger security screening checkpoints and has achieved certification to the TSA and European Civil Aviation Conference (ECAC) highest standards. There are more than 100 R&S QPS201 scanners deployed in US airports already, and more than 2,000 systems deployed in airports worldwide. The QPS201 uses safe millimetre wave radio frequency technology to rapidly and accurately screen passengers for concealed threats. The system requires only milliseconds per scan, and its open design and hands-down scan pose make security screening easy and accessible for travellers.

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Fluentgrid Ltd., a leading provider of utility digitalisation platforms and advanced grid management solutions, announced its joining the Wirepas ecosystem and completing full integration of its Head-End System (HES) with the Wirepas Certified platform.

This milestone allows utilities and AMI service providers to seamlessly deploy Wirepas-based networks using Fluentgrid’s proven HES, enabling scalable, multi-vendor smart electricity metering rollouts with assured data reliability and secure, standards-aligned performance. Fluentgrid has already initiated its first pilots on the integrated platform, with early results confirming strong interoperability and field readiness. The integration reinforces both companies’ commitment to supporting India’s RDSS program by ensuring solutions that directly address the needs of utilities and the realities of large-scale deployment.

“Fluentgrid has always been committed to providing utilities with open, flexible and future-
proof digital infrastructure,” said Vipresh Gannamani, Director, Fluentgrid. “By integrating our Head-End System with the Wirepas Certified platform, we are expanding the choice and
interoperability available to our customers. This collaboration ensures that utilities can adopt large-scale mesh deployments with confidence, supported by a robust, field-tested ecosystem, aligned with the national goal of enabling the RDSS vision.”

Wirepas CEO Teppo Hemiä commented:
“Fluentgrid’s integration brings tremendous value to the Wirepas ecosystem in India. A strong and interoperable Head-End System is essential for the scale the market demands. Their completed integration and ongoing pilots are proof of real progress towards open, multi-vendor smart metering architectures, and fully in line with our focus on supporting utilities and helping India achieve the ambitions of the RDSS program.”

The combined capabilities of Fluentgrid’s HES and the Wirepas Certified platform provide
utilities, AMISPs and system integrators with an ultra-resilient, infinitely scalable solution that accelerates deployment timelines while maintaining full transparency and interoperability across the value chain.

The post Fluentgrid Completes Wirepas Certified HES Integration, Joining The Growing Ecosystem For Smart Electricity Metering appeared first on ELE Times.

Cadence, a global leader in electronic system design, celebrated 20 years in Pune as a core research and development hub. This milestone marks two decades of sustained investment and innovation in the region. Established in 2006 by Tensilica, now part of Cadence, this anniversary marks the company’s early belief in Pune’s technology and engineering ecosystem during a period when few multinational technology companies operated there.

Starting with a five-member team, Cadence now employs over 300 employees in Pune and continues to scale its talent base. The Pune centre is a key part of the Silicon Solutions Group. Teams here develop highly complex digital signal processing (DSP) IP, AI accelerators, DDR, and mixed-signal IP for leading semiconductor and electronics companies worldwide. These technologies enable critical applications across consumer electronics, data centres, and automotive markets.

“As we celebrate 20 years in Pune, we take pride in the world-class IP teams here, who collaborate with our global teams to deliver products used by customers worldwide,” said Boyd Phelps, Senior Vice President and General Manager, Silicon Solutions Group at Cadence. “The continued growth of our Pune site emphasises Cadence’s confidence in the region’s talent and our ongoing commitment to investing in people, capabilities, and infrastructure across India.”

As it enters its third decade in Pune, the company remains dedicated to advancing cutting-edge silicon IP and nurturing local talent. Cadence actively partners with MeitY, AICTE, IITs, and over 400 universities to build a strong chip-design talent pipeline. It also supports startups through initiatives like Chips to Startup (C2S). Through advanced EDA tools and India-led innovations in AI-driven and chiplet-based design, Cadence is helping advance India’s semiconductor mission while accelerating global innovation.

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Anritsu and VTT Technical Research Centre of Finland have demonstrated a major advance in D-band wireless communications by validating a beam-steering transmit array antenna system using advanced test equipment. The achievement confirms the feasibility of stable, high-capacity wireless links for next-generation backhaul, industrial, defence and future 6G networks.

Using Anritsu’s precision test equipment and VTT’s steerable transmitarray antenna, the teams achieved high-speed wireless links across the 110–170 GHz D-band. Link performance and beam-steering behaviour were assessed under realistic over-the-air (OTA) conditions using wideband modulated signals up to 8 GHz bandwidth. This system-level characterisation, from signal generation to OTA performance, confirmed multi-gigabit data rates in the tens-of-Gbps range, including 20 Gbps over 1 m and reliable operation up to 7 m, setting a new benchmark for D-band connectivity.

The demonstration features a lightweight, scalable transmitarray antenna developed by VTT, incorporating advanced phase-shifting elements and vector-modulator MMICs. Its electronically steerable design provides rapid, precise beam control without mechanical movement, maintaining signal strength under changing conditions. Supported by Anritsu’s state-of-the-art test equipment, the results reflect a proven, instrumentation-grade measurement approach that ensures reliability and scalability for future deployments.

“Anritsu is proud to collaborate with VTT to advance the practical use of D-band wireless technology. Together, we have validated performance levels that bring high-frequency wireless links closer to real-world deployment,” said Jonathan Borrill, CTO, Test & Measurement, Anritsu.

“This milestone shows how strategic partnerships turn deep tech into a competitive advantage. By combining VTT’s steerable transmitarray expertise with Anritsu’s precise instrumentation‑grade validation, we shorten adoption cycles and scale D‑band from the lab to live networks — creating growth opportunities across critical infrastructure, manufacturing, defence, 6G and beyond,” said Tauno Vähä‑Heikkilä, Director, Strategic Partnerships, VTT.

Anritsu and VTT will now engage with industry partners to evaluate use cases and prepare the technology for upcoming field trials and deployments, marking a landmark step toward realising the potential of D-band wireless for next-generation networks.

The post Breakthrough in D-band Wireless: Anritsu and VTT Demonstrate World-Leading Transmit array-Based High-Speed Connectivity appeared first on ELE Times.

STMicroelectronics held a virtual media briefing, hosted by Patrick Aidoune, General Manager, General Purpose MCU Division at ST, on November 17, 2025. The briefing was held before their flagship event, the STM32 Summit, where they launched STM32V8, a new generation of STM32 microcontrollers.

STMicroelectronics introduced its new generation microcontroller, STM32V8, under the STM32 class recently. Built on an innovative 18nm process technology with FD-SOI and phase change memory (PCM) technology included, this microcontroller is the first of its kind in the world.  It is the first under 20nm process to use FD-SOI along with an embedded PCM technology.

FD-SOI Technology

The FD-SOI is a silicon technology, co-developed by ST, which brought innovation in the aerospace and automotive applications. The 18nm process, co-developed with the Samsung Foundry, provides a cost-competitive leap in both performance as well as power consumption.

The FD-SOI technology gives a strong robustness to ionising particles and reliability in harsh operating environments, which makes it particularly suitable for intense radiation exposure found in earth orbit systems. The FD-SOI also helps reduce the static power consumption, along with allowing operations on a lower voltage supply, while sustaining harsh industrial environments as well.

Key Features

STM32V8’s Arm Cortex-M85 core, along with the 18nm process, gives it a clock speed of up to 800MHz, making it the most powerful STM32 ever shipped. It has also been embedded with up to 4 Mbytes of user memory in a competitive dual bank, allowing bank swapping for seamless code updates.

Keeping in mind the needs of developers, the STM32V8 provides for more compute headroom, along with more security and improved efficiency. Compared it is 40nm process node with the same technologies, the STM32V8 brings with it improved performance, higher density, and better power efficiency.

Industrial Applications

This new microcontroller is a multipurpose system to benefit several industries:

• Factory Automation and Robotics
• Audio Applications
• Smart Cities and Buildings
• Energy Management Systems
• Healthcare and Biosensing
• Transportation (ebikes)

Achievements

ST’s new microcontroller has been selected by SpaceX for its high-speed connectivity system in the Starlink Satellite System.

“The successful deployment of the Starlink mini laser system in space, which uses ST’s STM32V8 microcontroller, marks a significant milestone in advancing high-speed connectivity across the Starlink network. The STM32V8’s high computing performance and integration of large embedded memory and digital features were critical in meeting our demanding real-time processing requirements, while providing a higher level of reliability and robustness to the Low Earth Orbit environment, thanks to the 18nm FD-SOI technology. We look forward to integrating the STM32V8 into other products and leveraging its capabilities for next-generation advanced applications,” said Michael Nicolls, Vice President, Starlink Engineering at SpaceX.

STM32V8, like its predecessors, is expected to draw significant benefit from ST’s edge AI ecosystem, which is under continued expansion. Currently, the STM32V8 is in early-stage access for selected customers with key OEMs’ availability as of the first quarter 2026 and with broader availability to follow.

Apart from unveiling the new generation microcontroller, ST also announced the expansion of its STM32 AI Model Zoo, which is part of the comprehensive ST Edge AI Suite of tools. The STM32 AI Model Zoo has more than 140 models from 60 model families for vision, audio, and sensing AI applications at the edge, making it the largest MCU-optimised library of its kind.

This AI Model Zoo has been designed, keeping in mind the requirements of both data scientists and embedded systems engineers, a model that’s accurate enough to be useful and that also fits within their energy and memory constraints.

The STM32 AI Model Zoo is the richest in the industry, for it not only offers multiple models, but also scripts to easily retrain models, evaluate accuracy, and deploy on boards. ST has also introduced native support for PyTorch models. This complements their existing support for TensorFlow, Keras AI frameworks, LiteRT, and ONNX formats, giving developers additional flexibility in their development workflow. They are also introducing more than 30 new families of models, which can use the same deployment pipeline. Many of these models have already been quantised and pruned, meaning that they offer significant memory size and inference time optimisations while preserving accuracy.

Additionally, they also announced the release of STM32 Sidekick, their new AI agent on the ST Community, available 24/7. This new AI agent is trained on official STM32 documentation (datasheets, reference manuals, user manuals, application notes, wiki entries, and community knowledge base articles) to help users locate relevant technical data, obtain concise summaries of complex topics, and discover insights and documents. Alongside, they announced STM32WL3R, a version of their STM32WL3 tailored for remote control applications supporting the 315 MHz band. The STM32WL3R is a sub-GHz wireless microcontroller with an ultra-low-power radio.

~ Shreya Bansal, Sub-Editor

The post Redefining Edge Computing: How the STM32V8 18nm Node Outperforms Legacy 40nm MCUs appeared first on ELE Times.

Union Electronics and IT Minister Ashwini Vaishnaw predicted that ‘Bharat’ will become a major player in the entire electronics stack, in terms of design, manufacturing, operating system, applications, materials, and equipment.

In an X post, the Union Minister drew attention to a major milestone for Prime Minister Narendra Modi’s ‘Make in India’ initiative and making India a major producer economy since Apple shipped $50 billion worth of mobile phones in 2025.

“Electronics production has increased six times in the last 11 years. And electronics exports have grown 8 times under PM Modi’s focused leadership. This progress has propelled electronics products among the top three exported items,” Vaishnaw noted.

He further informed that 46 component manufacturing projects, laptop, server, and hearable manufacturers had added to the ecosystem, which are making electronics manufacturing a major driver of the manufacturing economy.

“Four semiconductor plants will start commercial production this year. Total jobs in electronics manufacturing are now 25 lakh, with many factories employing more than 5,000 employees in a single location. Some plants employ as many as 40,000 employees in a single location,” the minister informed, adding that “this is just the beginning”.

Last week, the industry welcomed the approval of 22 new proposals under the third tranche of the Electronics Components Manufacturing Scheme (ECMS) by the government, saying that it marks a decisive inflexion point in India’s journey towards deep manufacturing and the creation of globally competitive Indian champions in electronics components.

With this, the total number of ECMS-approved projects rises to 46, taking cumulative approved investments to over Rs 54,500 crore. Earlier tranches saw seven projects worth Rs 5,532 crore approved on October 22 and 17 projects amounting to Rs 7,172 crore on November 17. The rapid scale-up across tranches underscores the strong industry response and the growing confidence in India’s components manufacturing vision.

According to the IT Ministry, the 22 projects approved in the third tranche are expected to generate production worth Rs 2,58,152 crore and create 33,791 direct jobs.

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EDOM Technology announced the introduction of the NVIDIA Jetson T4000 edge AI module, addressing the growing demand from system integrators, equipment manufacturers, and enterprise customers for balanced performance, power efficiency, and deployment flexibility. With powerful inference capability and a lightweight design, NVIDIA Jetson T4000 enables faster implementation of practical physical AI applications.

Powered by NVIDIA Blackwell architecture, NVIDIA Jetson T4000 supports Transformer Engine and Multi-Instance GPU (MIG) technologies. The module integrates a 12-core Arm Neoverse-V3AE CPU, three 25GbE network interfaces, and a wide range of I/O options, making it well-suited for low-latency, multi-sensor, and real-time computing requirements. In addition, Jetson T4000 features a third-generation programmable vision accelerator (PVA), dual encoders and decoders, and an optical flow accelerator. These dedicated hardware engines allow stable AI inference even under constrained compute and power budgets, making the platform particularly suitable for mid-range models and real-time edge applications.

For system integrators (SIs), the modular architecture of Jetson T4000, combined with NVIDIA’s mature software ecosystem, enables rapid integration of vision, sensing, and control systems. This significantly shortens development and validation cycles while improving project delivery efficiency, especially for multi-site and scalable edge AI deployments.

For equipment manufacturers, Jetson T4000’s compact form factor and low-power design allow flexible integration into a wide range of end devices, including advanced robotics, industrial equipment, smart terminals, machine vision systems, and edge controllers. These capabilities help manufacturers bring stable AI inference into products with limited space and power budgets, accelerating intelligent product upgrades.

Enterprise users can deploy Jetson T4000 across diverse scenarios such as smart factories, smart retail, security, and edge sensor data processing. By performing inference and data pre-processing at the edge, organisations can reduce system latency, lower cloud workloads, and improve overall operational efficiency—while maintaining system stability and deployment flexibility.

In robotics and automation applications, Jetson T4000 features low power consumption, high-speed I/O and a compact footprint, making it an ideal platform for small mobile robots, educational robots, and autonomous inspection systems, delivering efficient and reliable AI computing for a wide range of automation use cases.

NVIDIA Jetson product lineup spans from lightweight to high-performance modules, including Jetson T4000 and T5000, addressing diverse requirements ranging from compact edge devices and industrial control systems to higher-performance inference applications. With NVIDIA’s comprehensive AI development tools and SDKs, developers can rapidly port models, optimise inference performance, and seamlessly integrate AI capabilities into existing system architectures.

Beyond supplying Jetson T4000 modules, EDOM Technology leverages its extensive ecosystem of partners across chips, modules, system integration, and application development. Based on the specific development stages and requirements of system integrators, equipment manufacturers, and enterprise customers, EDOM provides end-to-end support—from early-stage planning and technical consulting to ecosystem enablement. By sharing ecosystem expertise and practical experience, EDOM helps both existing customers and new entrants to the edge AI domain quickly build application capabilities and deploy edge AI solutions tailored to real-world scenarios.

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Anritsu Corporation will exhibit Battery Cycler and Emulation Test System RZ-X2-100K-HG, planned for sale in the North American market as an evaluation solution for eMobility, at CES 2026 (Consumer Electronics Show), one of the world’s largest technology exhibitions to be held in Las Vegas, USA, from January 6 to January 9, 2026.

The launch of the RZ-X2-100K-HG in the North American market represents the first step in the global expansion efforts of TAKASAGO, LTD., which holds a significant share in the domestic EV development market, and it is an important measure looking ahead to future global market growth.

At CES 2026, a concept exhibition will showcase the Power HIL evaluation system combining the RZ-X2-100K-HG with dSPACE’s HIL simulator, demonstrating a new direction for the EV evaluation process.

Additionally, the power measurement solutions from DEWETRON, which joined the Anritsu Group in October 2025, will also be exhibited. Using a three-phase motor performance evaluation demonstration, we will present example applications.

The RZ-X2-100K-HG is a test system developed by TAKASAGO, LTD. of the Anritsu Group, equipped with functions for charge-discharge testing and battery emulation that support high voltage and large current. It is a model based on the RZ-X2-100K-H, which has a proven track record in Japan, adapted to comply with the United States safety standards and input power specifications. This system is expected to be used for testing the performance, durability, and safety of automotive batteries and powertrain devices in North America.

Power HIL (Power Hardware-in-the-Loop) is an extended simulation technology that combines virtual and real elements by adding a “real power supply function” to HIL (Hardware-in-the-Loop). Power HIL creates a virtual vehicle environment with real power, reproducing EV driving tests and charging tests compatible with multiple charging standards under conditions close to reality. This allows for high-precision and efficient evaluation of battery performance, safety, and charging compatibility without using an actual vehicle.

[*] Battery Emulation Test System

A technology that simulates the behaviour of real batteries (voltage, current, internal resistance, etc.) using a power supply device to evaluate how in-vehicle equipment operates.

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Keysight Technologies, Inc. introduced Keysight AI Software Integrity Builder, a new software solution designed to transform how AI-enabled systems are validated and maintained to ensure trustworthiness. As regulatory scrutiny increases and AI development becomes increasingly complex, the solution delivers transparent, adaptable, and data-driven AI assurance for safety-critical environments such as automotive.

AI systems operate as complex, dynamic entities, yet their internal decision processes often remain opaque. This lack of transparency creates significant challenges for industries, such as automotive, that must demonstrate safety, reliability, and regulatory compliance. Developers struggle to diagnose dataset or model limitations, while emerging standards — such as ISO/PAS 8800 for automotive and the EU AI Act- mandate explainability and validation without prescribing clear methods. Fragmented toolchains further complicate engineering workflows and heighten the risk of conformance gaps.

Keysight AI Software Integrity Builder introduces a unified, lifecycle-based framework that answers the critical question: “What is happening inside the AI system, and how do I ensure it behaves safely in deployment?” The solution equips engineering teams with the evidence needed for regulatory conformance and enables continuous improvement of AI models. Unlike fragmented toolchains that address isolated aspects of AI testing, Keysight’s integrated approach spans dataset analysis, model validation, real-world inference testing, and continuous monitoring.

Core capabilities of Keysight AI Software Integrity Builder include:

• Dataset Analysis: Analyse data quality using statistical methods to uncover biases, gaps, and inconsistencies that may affect model performance.
• Model-Based Validation: Explains model decisions and uncovers hidden correlations, enabling developers to understand the patterns and limitations of an AI system.
• Inference-Based Testing: Evaluates how models behave under real-world conditions, detects deviations from training behaviour, and recommends improvements for future iterations.

While open-source tools and vendor solutions typically address only isolated aspects of AI testing, Keysight closes the gap between training and deployment. The solution not only validates what a model has learned, but also how it performs in operational scenarios — an essential requirement for high-risk applications such as autonomous driving.

Thomas Goetzl, Vice President and General Manager of Keysight’s Automotive & Energy Solutions, said: “AI assurance and functional safety of AI in vehicles are becoming critical challenges. Standards and regulatory frameworks define the objectives, but not the path to achieving a reliable and trustworthy AI deployment. By combining our deep expertise in test and measurement with advanced AI validation capabilities, Keysight provides customers with the tools to build trustworthy AI systems backed by safety evidence and aligned with regulatory requirements.”

With AI Software Integrity Builder, Keysight empowers engineering teams to move from fragmented testing to a unified AI assurance strategy, enabling them to deploy AI systems that are not only performant but also transparent, auditable, and compliant by design.

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Courtesy: Orbit & Skyline

In the semiconductor ecosystem, we are familiar with the chips that go into our devices. Of course, they do not start as chips but are made into the familiar form once the process is complete. It is easy to imagine how to arrive at that end in silicon-based technology, but things are far more interesting in the III-V tech world. Here, we must first achieve the said III-V film using a thin-film deposition method. It is obvious that this would form the bedrock of the device, and quality is critical. Minimal defects, highest mobility, and a plethora of demands following the advent of technology have made this aspect extremely important in today’s world.

In this blog, we will cover how Molecular Beam Epitaxy (MBE) enables the growth of GaAs-based devices, its history, advantages, challenges, and the wide range of optoelectronic applications it supports. Looking to optimise thin-film growth or improve device yield? Explore our Semiconductor FAB Solutions for end-to-end support across Equipment, Process, and Material Supply.

What Is Molecular Beam Epitaxy (MBE)?

Molecular Beam Epitaxy (MBE) is a well-known thin-film growth technique developed in the 1960s. Using ultra-high vacuum (UHV) conditions, it grows high-purity thin films with atomic-level control over the thickness and doping concentration of the layers. This provides excellent control to tune device properties and, in the case of III–V films, bandgap engineering. Such sought-after features make MBE widely renowned for producing the best-quality films, which currently lead device performance in applications such as LEDs, solar cells, sensors, detectors, and power electronics.

However, its major drawbacks include high costs and slow growth rates, limiting large-scale industry adoption. Need support with MBE tool installation, calibration, or fab floor setup? Our Global Field Engineering and Fab Facility Solutions teams can help.

A Brief History of MBE Technology

The concept of Molecular Beam Epitaxy was first introduced by K.G. Günther in a 1958 publication. Even though his films were not epitaxial—being deposited on glass, John Davey and Titus Pankey expanded his ideas to demonstrate the now-familiar MBE process for depositing GaAs epitaxial films on single-crystal GaAs substrates in 1968.

The final version of the technology was given by Arthur and Cho in the late 1960s, observing the MBE process using a Reflection High Energy Electron Diffraction (RHEED) in-situ process. If you work with legacy MBE platforms or require upgrade support, our Legacy Tool Management Services ensure continuity and extended tool life.

Why GaAs? The First Semiconductor Grown by MBE

The first semiconductor material to be grown using MBE, gallium arsenide or GaAs for short, is one of the leading III-V semiconductors in high-performance optoelectronics such as solar cells, photodetectors, lasers, etc. Due to its several interesting properties, such as a high band gap of 1.43 eV, high mobility, high absorption coefficient, and radiation hardness, it finds use in sophisticated applications such as space photovoltaics as well as infrared detectors and next-generation quantum devices.

Since GaAs was the first material to be studied using the MBE method, it is far better understood with decades of research on devices. The efficiency of heterojunction solar cells grown on substrates such as Ge was as high as 15-20% in the 1980s. Although the current numbers are the best in the industry, using MBE for growing GaAs solar cells comes with its own set of challenges and advantages:

• Throughput and cost: Commercially, it is not as viable as some of the other vapor phase growth techniques since it is a slow and expensive process. Growth rates of MBE films are usually in the range of ~1.0 μm/h, which are far behind the CVD achieved rates of up to ~200 μm/h.
• Thickness and uniformity: Solar cell structures require absorber layers with thicknesses of the order of several microns. Maintaining uniformity over such a range is not trivial.
• Defect management: Thin films are beset with a range of defects such as dislocations, antisite defects, point defects, background impurities and so on. Optoelectronic devices suffer heavily due to the presence of defects as carrier lifetimes reduce and consequently open circuit voltage and fill factor. Therefore, multiple factors such as substrate quality, interface sharpness, and growth conditions are mandatory.
• Doping and alloy incorporation: MBE is one of the best techniques to dope and make alloys, especially when it comes to III-V compounds. Band gap engineering to expand the available bandwidth for solar absorption is one of the most important advantages of using MBE. When making multiple junctions or tandem cells, several growth challenges, such as phase separation, strain, and exact control of the composition of each layer, are challenging.
• Surface and interface quality: Interfacial strain is one of the major causes of loss of carriers due to recombination. When making solar cell stacks, there are multiple layers where interfaces are required, such as window layers, tunnel junctions, and passivation layers. MBE is excellent at providing abrupt interfaces due to its fast shutter speed and ultra-high vacuum conditions, resulting in high-performance devices.

A lot of the advantages of MBE are nullified due to its challenges, which makes it more of a hybrid technique when it comes to industrial applications. This has resulted in the usage of higher throughput methods, such as MOVPE/MOCVD, along with hybrid attempts to improve efficiency.

Other Optoelectronic Devices Grown Using MBE

In III-V materials and beyond, MBE has excelled in growing device-quality layers of several other types of optoelectronic structures:

• LASERs and VCSELs: One of the most grown stacks by MBE is of AlGaAs/GaAs heterostructure for quantum well lasers and vertical cavity surface emitting lasers (VCSELs). AlGaAs/GaAs multi-quantum well VCSELs with distributed Bragg reflectors (DBRs) have been successfully demonstrated with threshold currents, continuous wave operations at elevated temperatures, GHz modulation speeds, etc.
• Quantum Cascade LASERs (QCLs): The same GaAs/AlGaAs heterostructures have been fabricated for application in mid-infrared QCLs using MBE. Its specialty in producing abrupt interfaces and controlled doping is used in growth methods to reduce interface roughness and improve performance.
• Infrared Photodetectors: A leading IR photodetector currently is HgCdTe (MCT), which has been grown using MBE on GaAs substrates. GaSb-based nBn detectors are also grown using superlattices of InAs/GaSb, which reduces lattice mismatch due to buffer layers.
• High mobility 2D electron gas heterostructures: One of the most important discoveries of the last couple of decades has been that of 2-dimensional electron gas, which has led to applications such as high electron mobility transistor (HEMT). AlGaAs/GaAs heterostructures support the formation of this 2DEG, where the purity of the source material is critical. MBE grown films have shown mobilities as high as ~ 35 x 106 cm2/V.s.

Conclusion

MBE is a complex, slow process that has largely been confined to R&D labs traditionally. However, the quality of the deposited layers is unparalleled and has helped in improving and discovering new devices. In the last decade or so, there has been partial adoption of MBE in the industry due to the ability of the tool to provide cutting-edge device quality. However, mass adoption is unlikely due to the low quantity of wafers that are possible to grow at a time, and so we remain content with discovering the next generation of devices.

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Courtesy: Cadence

Are you still relying solely on simulation to validate your RTL design? Is there any more validation required?

Simulation has been a cornerstone of hardware verification for decades. Its ability to generate random stimuli and validate RTL across diverse scenarios has helped engineers uncover countless issues and ensure robust designs. However, simulation is inherently scenario-driven, which means certain rare corner cases can remain undetected despite extensive testing.

This is where formal verification adds significant value. Formal doesn’t just simply mathematically analyse the entire state space of your design; it checks every possible value and transition your design could ever encounter, providing exhaustive coverage that complements simulation. No corner case is left unchecked. No bug is left hiding. Together, they form a powerful verification strategy.

Why Formal Matters in Modern Validation

Any modern validation effort needs to take advantage of formal verification, where the apps in the Jasper Formal Verification Platform analyse a mathematical model of RTL design and find corner-case design bugs without needing test vectors. This can add value across the design and validation cycle. Let’s look at some standout Jasper applications: Jasper’s Superlint and Visualise can help designers to quickly find potential issues or examine RTL behaviours without formal expertise. Jasper’s FPV (Formal Property Verification) allows formal experts to create a formal environment and sign off on the IP, delivering the highest design quality and better productivity than doing block-level simulation. Jasper’s C2RTL is used to exhaustively verify critical math functions in CPUs, GPUs, TPUs, and other AI accelerator chips.

Jasper enables thorough validation in various targeted domains, including low power, security, safety, SoC integration, and high-level synthesis verification.

“The core benefit of formal exhaustive analysis is its ability to explore all scenarios, especially ones that are hard for humans to anticipate and create tests for in simulation.”

Why Formal? Why Now?

Here’s why formal verification matters now:

• No more test vectors or random stimuli. Formally, mathematically, and automatically explores all reachable states; verification can start as soon as RTL is available without the need to create a simulation testbench.
• Powerful for exploring corner-case bugs. Exhaustive formal analysis can catch corner case bugs that escape even the most creative simulation testbenches.
• Early design bring-up made easy. Validate critical properties and interfaces before your full system is ready.
• Debugging is a breeze. When something fails, formal provides a precise counterexample, often with the shortest trace, eliminating the need for endless log hunting.
• Perfect partnership with simulation. Simulation and formal aren’t rivals; they are partners. Use simulation for broad system-level checks, and Formal for exhaustive property checking and signoff of critical blocks. Merge formal and simulation coverage for complete verification signoff.

Conclusion

As RTL designs grow in complexity and stakes rise across power, safety, and performance, relying on simulation alone is no longer enough. While simulation remains indispensable for system-level validation, formal verification fills the critical gaps by exhaustively exploring every reachable state and uncovering corner-case bugs that would otherwise slip through. By integrating formal early and throughout the design cycle, teams can accelerate bring-up, improve debug efficiency, and achieve higher confidence at signoff. In today’s silicon landscape, the most robust verification strategy isn’t about choosing between simulation and formal—it’s about combining both to ensure no bug goes unnoticed and no risk is left unchecked.

The post Don’t Let Your RTL Designs Get Bugged! appeared first on ELE Times.

Courtesy: Synopsys

Competing in the booming data centre chip market often comes down to one factor: power efficiency. The less power a CPU, GPU, or AI accelerator requires to produce results, the more processing it can offer within a given power budget.

With data centres and their commensurate power needs growing exponentially, the energy consumption of each chip directly impacts the enormous costs of running gigawatt-scale AI data centres, where power and cooling account for 40–60% of operational expenditures.

To reduce the energy consumption of its workloads and gain a competitive edge, one software and cloud computing titan has made the strategic bet to design its own next-gen hyperscale System-on-Chip (SoC). By combining the advantages of new 2 nm-class process nodes with advanced, customised chip design techniques, the company is doubling down on the belief that innovation spanning process, design, and architecture can unlock new levels of power and cost efficiency.

Power play

To offer a compelling alternative in the market, the company knew that any new 2 nm design must push beyond the performance and efficiency process entitlement already baked into the scaling factors of the latest transistor fabrication methods. The transition to the 2 nm process is expected to provide 25–30% power reduction relative to the previous 3 nm node.

The company set an ambitious goal of achieving an additional 5% improvement on the 2 nm baseline. Through close collaboration with Synopsys — combining EDA software flow enhancements with our optimised Foundation IP logic library — the company exceeded its goal, achieving:

• 34% reduced power consumption with the same baseline flow.
• 51% reduced power consumption with an optimised flow.
• 5% silicon area advantage over baseline with ISO performance.

The company also evaluated our 2 nm embedded memories, which exceeded SRAM scaling expectations compared to our 3 nm product. On average, the 2 nm memory instances delivered 12% higher speed, occupied 8% less area, and consumed 12% less power than their 3 nm counterparts.

Expert collaboration

Because the transition to 2 nm comes with a shift from FinFET to GAA architecture, the company’s SoC developers faced a particularly steep learning curve, with an increase in complexity and technology assimilation.

They engaged our team in the early stages of the project — the byproduct of a trusted working relationship that spans more than four generations of AI chip designs — and even licensed our Foundation IP before the availability of any silicon reports.

The company used our IP, reference methodology, and Fusion Compiler tool to explore all commercially available options for achieving their power budget requirements. While the early development cycles produced the silicon area advantage, they did not achieve the power scaling targets the company sought.

Adaptation and optimisation

Seeking additional assistance, the company inquired whether our EDA tools and IP could be leveraged to push the design’s performance further.

R&D experts from our IP and EDA groups began collaborating on the design. Starting with the standard logic libraries, the IP group worked closely with the company’s designers to adapt and optimise the libraries with new cells and updated modelling. Over several iterations, the teams delivered the 7.34% power benefit, with Synopsys PrimePower used for final power analysis.

Our Technology and Product Development Group then helped the company take it a step further. By developing new algorithms for Fusion Compiler, and after many trials based on the latest recommended power recipe, design flow optimisations produced a 9.51% combined power benefit.

At the same time, our application engineers worked closely with the company to provide the best solution from our broad portfolio of memory compilers. Weighing performance requirements with power and area targets, we were able to extend the benefit of 2 nm beyond instance-level scaling. In one key scenario, power was reduced by an additional 25% by using an alternative configuration that met the 2 nm requirements.

Conclusion

As hyperscale compute continues its relentless push toward higher performance within ever-tighter power envelopes, success at advanced nodes like 2 nm will hinge on more than process scaling alone. This collaboration demonstrates how tightly integrated innovation across Foundation IP, EDA flows, and design methodology can unlock efficiency gains well beyond baseline node benefits. By adapting standard libraries, optimising tool algorithms, and co-engineering memory configurations, the company not only surpassed its power-efficiency targets but also achieved meaningful area and performance advantages. The outcome underscores a broader industry lesson: at 2 nm and beyond, early engagement, deep expertise, and holistic optimisation across the silicon stack will be critical to building the next generation of power-efficient hyperscale compute engines.

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Texas Instruments (TI) has introduced new automotive semiconductors and development resources to enhance safety and autonomy across vehicle models. TI’s scalable TDA5 high-performance computing system-on-a-chip (SoC) family offers power- and safety-optimised processing and edge artificial intelligence (AI) that supports up to Society of Automotive Engineers Level 3 vehicle autonomy. TI also unveiled the AWR2188, a single-chip, eight-by-eight 4D imaging radar transceiver, to help engineers simplify high-resolution radar systems. These devices, alongside the DP83TD555J-Q1 10BASE-T1S Ethernet physical layer (PHY), join TI’s broader automotive portfolio for next-generation advanced driver assistance systems (ADAS) and software-defined vehicles (SDVs). TI will be debuting these products at CES 2026, Jan. 6-9, in Las Vegas, Nevada.

“The automotive industry is moving toward a future where driving doesn’t require hands on the wheel,” said Mark Ng, director of automotive systems at TI. “Semiconductors are at the heart of bringing this vision of safer, smarter and more autonomous driving experiences to every vehicle. From detection and communication to decision-making, engineers can use TI’s end-to-end system offering to innovate what’s next in automotive.”

High-performance compute SoCs enable safe, scalable AI across vehicle models

To enhance safety and autonomy in next-generation vehicles, automakers are adopting central computing systems that support AI and sensor fusion for real-time decision-making. Designed for high-performance computing, TI’s TDA5 SoC family offers edge AI acceleration from 10 trillion operations per second (TOPS) to 1200 TOPS with power efficiency beyond 24 TOPS/W. This scalability, enabled by their chiplet-ready design with Universal Chiplet Interconnect Express interface technology, allows designers to implement different feature sets and support up to Level 3 autonomous driving using a single portfolio. Building on over two decades of experience in automotive processing, the family expands the performance of TI’s existing portfolio to enable automakers to centralise their computing architectures and process advanced AI models.

By integrating the latest generation of TI’s C7 neural processing unit (NPU), TDA5 SoCs provide up to 12 times the AI computing of previous generations with similar power consumption, eliminating the need for costly thermal solutions. This performance supports billions of parameters within language models and transformer networks, increasing in-vehicle intelligence while maintaining cross-domain functionality. The family features the latest Arm Cortex-A720AE cores, allowing automakers to integrate more safety, security and computing applications.

TDA5 SoCs reduce system complexity and costs by supporting cross-domain fusion of ADAS, in-vehicle infotainment and gateway systems within a single chip. Their safety-first architecture further simplifies systems by helping automakers meet Automotive Safety Integrity Level D safety standards without external components.

To simplify complex vehicle software management, TI is partnering with Synopsys to provide a Virtualiser development kit for TDA5 SoCs. The kit’s digital twin capabilities help engineers accelerate time-to-market for their SDVs by up to 12 months.

Single-chip, eight-by-eight radar transceiver achieves earlier, more accurate detection

With enhanced perception and reliability in any weather condition, radar is a fundamental technology for sophisticated ADAS and greater vehicle autonomy. Designed to meet global market needs, TI’s AWR2188 4D imaging radar transceiver integrates eight transmitters and eight receivers into a single launch-on-package chip. This integration simplifies higher-resolution radar systems because eight-by-eight configurations do not require cascading, while scaling up to higher channel counts requires fewer devices. The transceiver supports both satellite and edge architectures, offering automakers the flexibility to simplify and accelerate the global deployment of ADAS features across entry-level to premium vehicles.

The AWR2188 features enhanced analogue-to-digital converter data processing and a radar chirp signal slope engine, both supporting 30% faster performance than currently available solutions. This level of performance powers advanced radar use cases such as detecting lost cargo, distinguishing between closely positioned vehicles and identifying objects in high-dynamic-range scenarios. The transceiver can detect objects with greater accuracy at distances >350m, altogether enabling safer, more autonomous driving.

10BASE-T1S technology extends Ethernet to vehicle edge nodes

The acceleration toward SDVs and higher levels of autonomy is prompting a fundamental shift in subsystem architectures. Ethernet is an important enabler for this evolution, as it allows systems to collect and transmit more data across vehicle zones in real time through a simple, unified network architecture. TI’s new DP83TD555J-Q1 10BASE-T1S Ethernet Serial Peripheral Interface PHY with an integrated media access controller offers nanosecond time synchronisation, industry-leading reliability and Power over Data Line capabilities. These features enable engineers to extend high-performance Ethernet to vehicle edge nodes while reducing cable design complexity and costs.

With TI’s end-to-end system offering, which includes technologies for advanced sensing, reliable in-vehicle networking and efficient AI processing, automakers can develop systems that improve safety and automation levels across different vehicle models.

TI at CES 2026

In the Las Vegas Convention Centre North Hall, meeting room No. N115, TI will showcase how innovation across its analogue and embedded processing portfolios is reshaping what’s next in how people move, live and work. Demonstrations include advancements in vehicle technology and advanced mobility, smart homes and digital health, energy infrastructure, robotics, and data centres. See ti.com/CES.

Package, availability and pricing

• The TDA54 software development kit is now available on TI.com to help engineers get started with the TDA54 Virtualiser development kit. Samples of the TDA54-Q1 SoC, the first device in the family, will be sampled to select automotive customers by the end of 2026.
• Preproduction quantities of the AWR2188 transceiver and an evaluation module are now available upon request at TI.com.
• Preproduction quantities of the DP83TD555J-Q1 10BASE-T1S Ethernet PHY and an evaluation module are now available upon request at TI.com.

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Courtesy: Keysight Technologies

In radio frequency integrated circuits (RFICs), the high-frequency signals create unique phenomena that are not typically observed in regular digital and low-frequency analogue ICs. Even seemingly trivial design changes to an RFIC can degrade its behaviour and overall performance. As a result, rigorous simulations and verifications are essential after every modification without affecting team productivity and time-to-market.

If regular integrated circuit (IC) design itself is complex, imagine a niche that is an order of magnitude more complex. Even a tiny design change can drastically degrade their functionality and performance. The industry often uses terms like black magic and wizardry for them.

Yes, we’re talking about the esoteric art of designing radio frequency ICs (RFICs) and their even more sensitive cousins in the microwave and millimetre wave (mmWave) bands. In this post, we explain the specialised EDA tools that provide the rigorous simulations and validations required for designing RFICs, monolithic microwave ICs (MMICs), and mixed-signal ICs.

What are EDA tools for RF and mixed-signal IC design?

EDA tools for RF and mixed-signal IC design accelerate the design of RF and mixed-signal semiconductor devices using in-depth knowledge of all the complex phenomena and effects that occur in these high-frequency analogue circuits. These circuits are typically composed of wafer-level power amplifiers, oscillators, filters, mixers, modulators, demodulators, antennas, transmission lines, and impedance-matching networks.

Some of the common phenomena that engineers must design for are signal degradation, electromagnetic interference (EMI), crosstalk, parasitic effects, and antenna effects due to the high-frequency signals used in RF, microwave, and mmWave use cases.

In this context, RF conventionally ranges from tens of megahertz (MHz) up to 3 gigahertz (GHz), covering most wireless communications (like Wi-Fi, 2G/3G/4G telecom, and Bluetooth), satellite communications, and global positioning systems. The microwave band of 3-30 GHz is used by radars and Wi-Fi 5/6/7. The mmWave 30-300 GHz band is used for 5G/6G telecom, 802.11ad gigabit Wi-Fi, radars, and automotive vehicle-to-everything.

To predict high-frequency behaviours and mitigate their effects, these specialised computer-aided design and automation software provide features like:

• simulating all the high-frequency effects of various alternating and direct current waveforms
• predicting scattering parameters (S-parameters) from circuit schematics
• modelling RF-relevant aspects of the physical layouts of chips, such as the antenna effects of interconnects and bonding wires
• predicting parasitic behaviours from the physical layouts of chips

Why are EDA tools essential for RF and mixed-signal IC design?

F and mixed-signal IC design is very different — typically even more rigorous and cautious — compared to regular IC design, which is an already rigorous workflow. The sections below outline these differences.

Careful analysis of every design change

Everything in RFIC design is much more sensitive to even trivial changes. At high frequencies, every interconnect and bonded wire is a radiating antenna that adds noise. Every capacitor exhibits inductance, and every inductor has capacitance. Even a small change in component specification, layout, or packaging can drastically attenuate a signal.

So, predictive simulations after every change are essential, starting from the schematic stage itself through the physical layout right up to tape-out. In fact, even the post-packaging stages are simulated because the packaging, as well as printed circuit board components around an RFIC, can affect its RF performance.

Different metrics

Since mixed-signal ICs process digitally modulated signals, they require metrics like error vector magnitude (EVM) instead of the traditional P1dB or third-order intercept point (IP3) analogue specifications. EDA tools must facilitate the tuning and optimisation of EVM at the circuit level.

More complex fabrication

RFIC fabrication is different in every way.

First, the semiconductor materials are different, which requires unique device models. For example, regular digital ICs use silicon with simple complementary metal oxide semiconductor (CMOS) processes. In contrast, RFICs use silicon germanium in BiCMOS configurations, and MMICs prefer III-V materials like gallium arsenide, indium phosphide, and gallium nitride.

Second, there are no simple standardised cell libraries like in digital ICs. Miniaturisation of passive components is unique to every RFIC design. Resistors are typically implemented as diffused regions in the semiconductor substrate and adjusted by changing dimensions and material properties. Capacitors are formed using overlapping metal layers with an insulating dielectric layer in between or metal-insulator-metal structures. Inductors are created using spiral metal traces on the die.

For these reasons, RFIC fabrication is offered by foundries that specialise in RFICs, MMICs, and III-V semiconductors. When designing an RFIC, EDA tools must consider how these components will interact, their parasitic effects, and other high-frequency phenomena.

System design budgets

Most of the systems that RFICs and mixed-signal ICs go into often involve stringent regulations and standards. So the system-level specifications impose budgets on parameters like the noise figure, power, phase noise, harmonics, linearity, and more. These budget constraints are passed down to the RF designers.

To satisfy these complex constraints without affecting signal integrity and performance, EDA tools are essential.

How are EDA tools used in the design of RFIC and mixed-signal ICs?

EDA tools are typically used as follows:

• Circuit simulations: These are computational techniques to model and predict the behaviour of electronic circuits based on their schematics. Mathematical equations or models describe the behaviour of each component under different operating conditions. After modelling the circuit, simulation software is used to solve the equations and predict key characteristics of the circuit’s behaviour. The increasingly complex and dense designs of modern RFICs require complex simulators capable of handling large, intricate circuits.
• Stability analysis: High-frequency transistors complicate the design flow for stable circuits. Instability problems can emerge at lower frequencies due to significant increases in gain. EDA tools allow stability analysis of amplifiers.
• EM co-simulation: These simulations allow the EM characterisation for every component of the design. The circuit designer can perform 3D EM analysis and EM-circuit co-simulations iteratively throughout the design phase.

What are the differences between digital and mixed-signal IC EDA tools for simulation, synthesis, and verification?

The design workflows for RFICs and mixed-signal ICs are very different from digital IC workflows, as outlined below.

Synthesis

Digital circuits consist of a small number of well-defined logic gates (like NAND). The circuit schematic is converted to a gate-level netlist expressed in a hardware description language like Verilog or very high-speed integrated circuit hardware description language (VHDL). During routing and placement, these gate-level constructs are then converted to on-wafer cells defined by the cell libraries in the selected fab’s process development kit (PDK).

In contrast, RF and mixed-signal ICs are analogue circuits with unique custom designs. The arrangements of resistors, inductors, capacitors, and active components into amplifiers, mixers, or other subsystems are often unique to each IC. They are not readily distillable into standard cells like digital gates are.

Instead, each subsystem is individually converted into on-wafer structures and interconnects. For example, an oscillator may utilise a complex configuration of transistors in feedback loops. The foundry PDKs for RFICs do provide process information, design rules, and models for active and passive components, but they are not as simple or standardised as digital ICs.

Simulation

In digital chip design, the digital nature of signals makes simulations relatively simpler. Digital IC simulations include various digital inputs and timing analyses.

In contrast, RFICs must contend with an infinite set of continuous high-frequency waveforms. Noise, parasitic effects, electromagnetic interference (EMI), and antenna effects emerge from the circuit arrangements as well as the physical on-wafer structures. So, realistic simulations are required at every step throughout the design cycle.

Verification

Design-rule checks (DRC) and implementation using a field-programmable gate array (FPGA) are common in digital IC design.

DRC verification tools are used in RFIC and mixed-signal semiconductor design as well. However, prototyping an RFIC with an FPGA is rare because its RF characteristics will be totally different. FPGAs are still used to verify the digital portions of mixed-signal ICs.

How do you choose the right EDA tool for a wireless electronic design project?

In addition to accurate RF and EM modelling and simulations, an essential feature is the ability to test the designs against wireless standards (like 5G and 802.11ad) right from the start.

This is possible using design tools that include virtual test benches (VTBs) for all the major wireless standards. VTBs ensure that the designs stay within the thresholds that standards place on signal power, noise, interference, and more.

What are the main challenges faced when using EDA tools in complex 3D circuit design?

Some of the main challenges in 3D RF circuit design include:

• modelling electromagnetic behaviours in 3D
• impedance matching
• noise
• linearity
• stability
• power consumption
• electromagnetic interference
• problems caused by increasing densification, like integrating different materials, 3D integration, and advanced packaging

 What are the key features to look for in EDA tools for effective RFIC Design?

For effective design of RFICs and mixed-signal ICs, look for these key features:

• Advanced simulation algorithms developed for Keysight RF instrumentation and Keysight RF EDA software ensure equivalent results between virtual simulations and physical measurements.
• Fast-envelope techniques, compact test signals, and distortion EVM make simulation run times practical for RFIC designs in data-intensive, high-bandwidth mmWave or sub-THz applications, including 5G/6G, electric vehicles, and AI-enabled systems.
• Authentic waveforms and VTBs incorporate system context when simulating designs, enabling teams to optimise RFIC designs for their intended system applications sooner and successfully integrate first-pass into physical devices and systems.

Keysight RFIC EDA tools

Keysight’s ecosystem of EDA tools streamlines RFIC and mixed-signal IC design, simulation, and verification. For large design teams looking for high productivity, the Keysight designcloud enables offloading RF and EM simulations to high-performance cloud platforms for rapid results and quick design cycles. The main design and simulation tools for RFICs and mixed-signal ICs are outlined below.

Keysight Advanced Design System (ADS)

The Advanced Design System is tailored for high-frequency RFIC and mixed-signal IC design and simulation. It achieves 3D heterogeneous integration (3DHI) of RFICs, MMICs, packaging, PCBs, and antennas using multi-technology modules. Key features are listed below.

• Multi-technology integration: ADS enables comprehensive 3D integration of chips, packaging, interconnects, and boards, facilitating realistic simulations of assembled products.
• Superior simulation capabilities: The platform’s advanced circuit-level RF simulations and EVM optimisations for mixed-signal ICs produce predictions that are close to real-world measurements.
• Comprehensive component libraries: The platform offers vendor component libraries and PDKs that include symbols and layout footprints, as well as high-accuracy RF and microwave models.
• Standards compliance: ADS enables verification against wireless standards like 5G, automotive radar, and 802.11ad.
• Flexible automation: ADS supports Python scripting for automation and integration with external applications like artificial intelligence (AI) and machine learning (ML) to streamline design and verification workflows.

RFIC Design (RFPro Circuit)

RFPro Circuit is a specialised software for RFIC design. It can:

• model components on silicon chips accurately
• optimise designs with sweeps and load-pull analysis
• simulate RF designs in the Cadence Virtuoso and Synopsys Custom Compiler environments
• increase performance using Monte Carlo and yield analysis
• Assess error vector magnitude (EVM) for the latest communication standards early in the design phase
• Use the latest foundry technology immediately

Keysight RFPro

RFPro enables RFIC and MMIC designers to run interactive electromagnetic-circuit co-simulation for tuning and optimising their circuits. It includes 3D planar and full 3D finite element method EM simulators.

How are AI and ML algorithms integrated into Keysight RFIC EDA tools?

PathWave ADS provides Python application programming interface (API) endpoints for integrating AI frameworks like TensorFlow and PyTorch. This enables the use of advanced artificial neural network models in the design, modelling and simulation workflows.

How is Python integrated with Keysight EDA tools?

Keysight EDA tools provide Python API endpoints that expand custom features and improve usability.

For example, Python scripts can be used to control data analysis, simulators, and processes.

Python scripts enable the training of custom AI-based simulation models that are trained on measured or published data.

The post EDA Tools for Robust RFICs and Mixed-Signal ICs appeared first on ELE Times.

Courtesy: Jessy Cavazos, 6G Solutions Expert, Keysight Technology

As the world prepares for the next leap in wireless technology, India is shaping a bold and inclusive vision for 6G, one that goes beyond speed and latency to address real-world challenges. In a recent interview, Mohmedsaeed Mombasawala, Keysight’s General Manager for Industry Solutions in India, and a key contributor to 6G research efforts in India, shared insights into how the country is approaching 6G with a unique blend of pragmatism, innovation, and social impact.

A Use-Case First Philosophy

India’s 6G strategy is fundamentally use-case driven, a departure from traditional infrastructure-first rollouts. Rather than focusing solely on technical specifications or spectrum availability, the country is prioritising solutions that address societal needs, especially in sectors like agriculture, healthcare, and logistics.

This approach is particularly relevant for India’s vast and diverse population, where connectivity gaps persist in rural and remote areas. Mombasawala emphasised that 6G must be more than a technological upgrade: it must be a platform for transformation.

“We’re not just building networks. We’re building solutions for farmers, doctors, and supply chain operators,” he explained.

By anchoring 6G development in real-world applications, India aims to ensure that the technology delivers tangible benefits to communities that have historically been underserved by previous generations of wireless infrastructure.

AI-Native Networks: Intelligence at the Core

One of the most exciting aspects of India’s 6G vision is the emphasis on AI-native radio access networks (RAN). In this model, artificial intelligence isn’t just a tool; it’s a foundational design element. AI will be embedded throughout the network, enabling dynamic spectrum allocation, predictive maintenance, and real-time optimisation of resources.

This shift reflects India’s strength in software and data science, positioning the country to play a key role in intelligent network design. It also aligns with global trends toward more autonomous and adaptive systems, where networks can learn, evolve, and respond to changing conditions without human intervention.

“AI will be central to how we manage, scale, and secure 6G networks,” Mombasawala noted. “It’s not just about efficiency, it’s about enabling new capabilities.”

Spectrum Strategy: Balancing Reach and Performance

While many countries are exploring high-frequency bands for ultra-fast data rates, India is taking a pragmatic approach to spectrum. The focus is on frequency range 3 (FR3) bands, which offer a balance between performance and coverage. These midband frequencies are well-suited for India’s geographic and demographic diversity, allowing for a broader reach without the need for dense infrastructure.

This strategy reflects a deep understanding of India’s connectivity landscape, where rural access remains a critical challenge. By prioritising spectrum that supports ubiquitous coverage, India is ensuring that 6G can serve both urban innovation hubs and remote villages.

Collaborative R&D and Global Engagement

India’s 6G efforts are deeply collaborative, involving academia, startups, industry leaders, and government agencies. Mombasawala highlighted the importance of cross-sector partnerships in driving innovation and ensuring that 6G solutions are both technically robust and socially relevant.

At the same time, India is actively participating in global standardisation efforts, contributing to international dialogues while tailoring its approach to local needs. This dual strategy—global alignment with local customisation—is key to building a 6G ecosystem that is both interoperable and inclusive.

A Blueprint for Inclusive Innovation

India’s vision for 6G offers a compelling blueprint for countries seeking to balance technology innovation with social impact. By focusing on use cases, AI-native design, and inclusive spectrum planning, India is not just preparing for 6G; it’s redefining what 6G can be.

This approach challenges the notion that next-generation technology must be exclusive or elite. Instead, it positions 6G as a tool for empowerment, capable of transforming lives and industries across the socioeconomic spectrum.

“We want 6G to be a catalyst for change,” Mombasawala concluded. “Not just in how we connect, but in how we live, work, and grow.”

The post India’s Vision for 6G: Use-Case Driven Innovation and AI-Enabled Networks appeared first on ELE Times.

Before examining the challenges and opportunities in bioelectronics, it is essential to understand how electronics and the human body converge. The human body—intrinsically organic and biological—can now interact with silicon-based systems through chips, interfaces, and digital extensions that do more than observe; they actively influence physiological function and enable measurable outcomes.

At its core, the human nervous system is among the most sophisticated electrical networks known. Every sensation, movement, and cognitive process originates as an electrical impulse transmitted across billions of neurons. Bioelectronics builds on this foundation by developing electronic systems capable of reading, interpreting, and modulating these neural signals with high precision.

At the centre of this convergence lies neural interface technology—where electronics, materials science, neuroscience, and computation intersect. What began as experimental neural signal recording has evolved into intelligent, closed-loop systems designed to interact with the nervous system in clinically and functionally meaningful ways.

The Nervous System: An Electrical Network

To call the human nervous system an electrical network is to acknowledge the fact that neurons communicate via action potentials—brief voltage changes produced by ionic movement across cell membranes. These electrical impulses propagate along nerve fibres and form the basis of basic human senses, including perception, motion, and cognition.

However, from an engineering standpoint, neural activity resembles a signal source but with certain riders. The biological signals that the neurons communicate through are characterized distinctly from conventional electronic systems in the following ways: 

• Extremely low amplitude (microvolt range)
• Highly variable across individuals
• Sensitive to physiological and environmental conditions
• Embedded in a noisy, living medium

These complexities change the entire course of the environment and approach, which is a crucial aspect of the electronics system design, and that’s why bioelectronics is not simply applied electronics—it is fundamentally a new class of system design.

The Neural Interface Challenge

A neural interface is the physical and electrical bridge between living tissue and electronic systems. Basically, it is a technological innovation that enables a direct communication pathway between the brain (nervous system) and an external device, allowing thoughts/neural signals to control machines, and machines to send sensory data back to the brain. Its primary functions are twofold:

1. Reading neural signals
2. Delivering electrical stimulation

This makes the landscape more challenging, owing to the complex mix of an artificial arrangement to be balanced with a natural or organic system, and making it perform efficiently and adaptively. To this effect, the challenge lies at the interface level itself. Electronics are rigid and static; biological tissue is soft, adaptive, and reactive. Any long-term interface must balance electrical performance with biocompatibility.

Key Challenges in such an arrangement include:

• Mechanical mismatch between electrodes and tissue
• Immune responses that degrade signal quality
• Corrosion in ionic biological environments
• Stability over years, not months

Dr. Ben Rapoport, Co-founder and Chief Science Officer of Precision Neuroscience- the rival of Neuralink, notes that innovation is increasingly focused on minimising invasiveness: “That’s a big misconception, he said. People often incorrectly assume that ‘you need electrodes that penetrate deep inside the brain to get that information out.”

He further adds that Precision lies in developing a thin film that sits on the brain and records the brain activity. This reflects a broader industry trend toward surface-level and flexible neural interfaces rather than deep, rigid implants.

Neural Interfaces Making Gradual Emergence

Industry experts increasingly emphasise that neural interfaces are no longer speculative technologies. Yet these are gradually making space in our lives, making them more common in overall human behavior. 

According to Kevin Hughes, Information Scientist of the Chemical Abstracts Service (CAS), which tracks emerging science and engineering domains, “With the recent news that Neuralink is approved to begin human trials, it’s clear that bioelectronics like brain-computer interfaces (BCIs) are moving out of the realm of science fiction and into reality.”

In the very same continuity, he also points out the difference that the industry is witnessing between the number of journals and the number of patents filed in the biotechnology landscape. The following graph shows the starkly lower number of commercial patents, while academic research has outpaced the same in the last 5 years. He writes, “his suggests that large-scale commercialization of these technologies faces fundamental scientific challenges that are being studied at the academic level and may be years away.”

Capturing Neural Signals: An Analog Problem

Neural signal acquisition is fundamentally constrained by analog design challenges. Electrical signals captured at neural electrodes typically exist in the microvolt range, making them highly susceptible to corruption from noise sources such as muscle activity, motion-induced artefacts, electrode impedance variability, and external electromagnetic interference. Unlike many conventional sensing environments, the biological interface itself is dynamic, lossy, and electrically unstable.

To this effect, the front-end electronics gets to play the most decisive role. These must provide:

• Ultra-low-noise amplification
• High input impedance
• Strong common-mode rejection
• Extremely low power operation

Unlike conventional sensors, neural interfaces cannot rely on static calibration. Signal properties drift over time due to biological adaptation and tissue response, demanding adaptive hardware and software co-design. Consequently, effective neural signal acquisition increasingly relies on adaptive architectures, where analog hardware, digital signal processing, and software algorithms are co-designed to track signal drift, compensate for variability, and maintain reliable performance over extended operational lifetimes.

From Signals to Interpretation

Raw neural signals carry no explicit meaning. Interpretation requires computational models capable of identifying patterns associated with intention, perception, or pathology. This has made way for Machine learning, hence making it central to modern bioelectronics. Models must continuously adapt as neural signals evolve, making on-device intelligence essential and timely.

According to BIOS Health, a company focused on AI-driven neural interfaces, this represents a new data modality in medicine:

 “At BIOS, we’re developing AI-powered neural interfaces to allow us to read and write neural signals as a new data modality in healthcare… we can decode it, use biomarkers to see how a disease is progressing, and we can change those electrical signals. In doing so, we’ve delivered a therapeutic—we’ve treated a disease.”

This highlights a shift from monitoring systems to active bioelectronic therapies. 

Writing to the Nervous System: Electrical Stimulation

Neural interfaces are not limited to passively observing neural activity; they are equally defined by their ability to actively influence the nervous system. This capability is most clearly demonstrated in modern bioelectronic devices that deliver precisely controlled electrical stimulation to targeted neural pathways. By injecting carefully shaped electrical pulses, these systems can alter neural firing patterns in ways that restore, suppress, or modulate biological function.

Electrical stimulation underpins a wide range of therapeutic and functional outcomes, including: 

• Restoration of sensory input
• Modulation of dysfunctional neural circuits
• Enablement of motor control
• Suppression of chronic pain signals

From an engineering standpoint, effective neural stimulation demands precise control over parameters such as pulse amplitude, width, frequency, and waveform shape. These parameters must be tailored not only to the targeted neural population but also to long-term safety constraints, including charge balancing and tissue compatibility. Overstimulation risks tissue damage or neural fatigue, making precision and reliability non-negotiable design requirements.

Increasingly, neural stimulation systems are evolving into closed-loop architectures, where real-time sensing, on-device computation, and adaptive stimulation form a continuous feedback cycle. Instead of delivering fixed stimulation patterns, these systems dynamically adjust outputs based on measured neural responses, enabling more personalised, efficient, and clinically effective interventions. This shift from open-loop to closed-loop control represents a critical step toward truly intelligent bioelectronic systems.

Case Study: Cochlear Implants

Cochlear implants remain one of the most successful examples of bioelectronics in practice. Rather than amplifying sound acoustically, cochlear implants convert audio signals into electrical stimulation patterns delivered directly to the auditory nerve. Frequency components are mapped spatially along an electrode array implanted in the cochlea.

Despite delivering a simplified representation of sound, cochlear implants exploit the brain’s neural plasticity. Over time, users learn to interpret these electrical patterns as meaningful auditory experiences. From an engineering perspective, cochlear implants demonstrate:

• Long-term biocompatibility
• Ultra-low-power embedded processing
• Robust signal mapping
• Effective closed-loop adaptation

They validate the principle that bioelectronics succeeds when it works with biology rather than attempting to replicate it perfectly. 

Power, Reliability, and Longevity

Implanted bioelectronic systems must operate reliably for years without failure. Power consumption, heat dissipation, and battery safety are critical constraints.

Unlike consumer electronics, failure carries direct clinical risk. As a result, bioelectronic design prioritises stability, redundancy, and conservative validation over rapid iteration.

Conclusion

The future of bioelectronics lies in deeper integration and softer interfaces—flexible electronics, bio-compatible materials, and adaptive systems that learn continuously. As silicon systems become more biologically aware, neural interfaces are evolving from experimental tools into foundational technologies for healthcare and human–machine interaction.

Bioelectronics does not aim to replace the nervous system. It aims to understand it—and, where possible, support it—using electronics designed to operate on biology’s terms.

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